Recursive partitioning models for linkage in COGA data
Bibliographic record
Abstract
We have developed a recursive-partitioning (RP) algorithm for identifying phenotype and covariate groupings that interact with the evidence for linkage. This data-mining approach for detecting gene x environment interactions uses genotype and covariate data on affected relative pairs to find evidence for linkage heterogeneity across covariate-defined subgroups. We adapted a likelihood-ratio based test of linkage parameterized with relative risks to a recursive partitioning framework, including a cross-validation based deviance measurement for choosing optimal tree size and a bootstrap sampling procedure for choosing robust tree structure. ALDX2 category 5 individuals were considered affected, categories 1 and 3 unaffected, and all others unknown. We sampled non-overlapping affected relative pairs from each family; therefore, we used 144 affected pairs in the RP model. Twenty pair-level covariates were defined from smoking status, maximum drinks, ethnicity, sex, and age at onset. Using the all-pairs score in GENEHUNTER, the nonparametric linkage tests showed no regions with suggestive linkage evidence. However, using the RP model, several suggestive regions were found on chromosomes 2, 4, 6, 14, and 20, with detection of associated covariates such as sex and age at onset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".